Overview

Dataset statistics

Number of variables12
Number of observations348
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.8 KiB
Average record size in memory96.4 B

Variable types

Categorical2
Numeric10

Alerts

year has constant value "2016" Constant
temp_2 is highly correlated with month and 6 other fieldsHigh correlation
temp_1 is highly correlated with month and 6 other fieldsHigh correlation
average is highly correlated with month and 7 other fieldsHigh correlation
actual is highly correlated with month and 7 other fieldsHigh correlation
forecast_noaa is highly correlated with month and 7 other fieldsHigh correlation
forecast_acc is highly correlated with month and 7 other fieldsHigh correlation
forecast_under is highly correlated with month and 7 other fieldsHigh correlation
friend is highly correlated with month and 5 other fieldsHigh correlation
week is highly correlated with yearHigh correlation
year is highly correlated with weekHigh correlation
month is highly correlated with temp_2 and 7 other fieldsHigh correlation
day is uniformly distributed Uniform
week is uniformly distributed Uniform

Reproduction

Analysis started2022-09-27 15:32:36.421122
Analysis finished2022-09-27 15:32:58.236564
Duration21.82 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

year
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2016
348 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1392
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016348
100.0%

Length

2022-09-27T22:32:58.331110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-27T22:32:58.483564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2016348
100.0%

Most occurring characters

ValueCountFrequency (%)
2348
25.0%
0348
25.0%
1348
25.0%
6348
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1392
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2348
25.0%
0348
25.0%
1348
25.0%
6348
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common1392
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2348
25.0%
0348
25.0%
1348
25.0%
6348
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2348
25.0%
0348
25.0%
1348
25.0%
6348
25.0%

month
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.477011494
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-27T22:32:58.603052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.498380066
Coefficient of variation (CV)0.5401225657
Kurtosis-1.236715006
Mean6.477011494
Median Absolute Deviation (MAD)3
Skewness0.03675217283
Sum2254
Variance12.23866309
MonotonicityIncreasing
2022-09-27T22:32:58.734961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
131
8.9%
331
8.9%
531
8.9%
731
8.9%
1231
8.9%
430
8.6%
630
8.6%
1030
8.6%
1130
8.6%
928
8.0%
Other values (2)45
12.9%
ValueCountFrequency (%)
131
8.9%
226
7.5%
331
8.9%
430
8.6%
531
8.9%
630
8.6%
731
8.9%
819
5.5%
928
8.0%
1030
8.6%
ValueCountFrequency (%)
1231
8.9%
1130
8.6%
1030
8.6%
928
8.0%
819
5.5%
731
8.9%
630
8.6%
531
8.9%
430
8.6%
331
8.9%

day
Real number (ℝ≥0)

UNIFORM

Distinct31
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.51436782
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-27T22:32:58.887851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.772981867
Coefficient of variation (CV)0.5654746601
Kurtosis-1.195389922
Mean15.51436782
Median Absolute Deviation (MAD)8
Skewness0.04703994115
Sum5399
Variance76.96521084
MonotonicityNot monotonic
2022-09-27T22:32:59.043765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1612
 
3.4%
912
 
3.4%
2312
 
3.4%
1512
 
3.4%
2812
 
3.4%
1212
 
3.4%
1012
 
3.4%
1112
 
3.4%
812
 
3.4%
712
 
3.4%
Other values (21)228
65.5%
ValueCountFrequency (%)
111
3.2%
211
3.2%
312
3.4%
412
3.4%
512
3.4%
612
3.4%
712
3.4%
812
3.4%
912
3.4%
1012
3.4%
ValueCountFrequency (%)
316
1.7%
3010
2.9%
2910
2.9%
2812
3.4%
2711
3.2%
2611
3.2%
2511
3.2%
2411
3.2%
2312
3.4%
2211
3.2%

week
Categorical

HIGH CORRELATION
UNIFORM

Distinct7
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Tues
52 
Fri
50 
Sat
50 
Sun
49 
Mon
49 
Other values (2)
98 

Length

Max length5
Median length3
Mean length3.431034483
Min length3

Characters and Unicode

Total characters1194
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFri
2nd rowSat
3rd rowSun
4th rowMon
5th rowTues

Common Values

ValueCountFrequency (%)
Tues52
14.9%
Fri50
14.4%
Sat50
14.4%
Sun49
14.1%
Mon49
14.1%
Wed49
14.1%
Thurs49
14.1%

Length

2022-09-27T22:32:59.213278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-27T22:32:59.400434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
tues52
14.9%
fri50
14.4%
sat50
14.4%
sun49
14.1%
mon49
14.1%
wed49
14.1%
thurs49
14.1%

Most occurring characters

ValueCountFrequency (%)
u150
12.6%
T101
 
8.5%
e101
 
8.5%
s101
 
8.5%
r99
 
8.3%
S99
 
8.3%
n98
 
8.2%
F50
 
4.2%
i50
 
4.2%
a50
 
4.2%
Other values (6)295
24.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter846
70.9%
Uppercase Letter348
29.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u150
17.7%
e101
11.9%
s101
11.9%
r99
11.7%
n98
11.6%
i50
 
5.9%
a50
 
5.9%
t50
 
5.9%
o49
 
5.8%
d49
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
T101
29.0%
S99
28.4%
F50
14.4%
M49
14.1%
W49
14.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1194
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u150
12.6%
T101
 
8.5%
e101
 
8.5%
s101
 
8.5%
r99
 
8.3%
S99
 
8.3%
n98
 
8.2%
F50
 
4.2%
i50
 
4.2%
a50
 
4.2%
Other values (6)295
24.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u150
12.6%
T101
 
8.5%
e101
 
8.5%
s101
 
8.5%
r99
 
8.3%
S99
 
8.3%
n98
 
8.2%
F50
 
4.2%
i50
 
4.2%
a50
 
4.2%
Other values (6)295
24.7%

temp_2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.65229885
Minimum35
Maximum117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-27T22:32:59.592926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile43.35
Q154
median62.5
Q371
95-th percentile81.65
Maximum117
Range82
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.16539811
Coefficient of variation (CV)0.194173212
Kurtosis0.3109757684
Mean62.65229885
Median Absolute Deviation (MAD)8.5
Skewness0.2530289628
Sum21803
Variance147.9969111
MonotonicityNot monotonic
2022-09-27T22:32:59.778331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6016
 
4.6%
6815
 
4.3%
5714
 
4.0%
6513
 
3.7%
7112
 
3.4%
5512
 
3.4%
6711
 
3.2%
5211
 
3.2%
6411
 
3.2%
5911
 
3.2%
Other values (46)222
63.8%
ValueCountFrequency (%)
352
 
0.6%
361
 
0.3%
393
0.9%
405
1.4%
413
0.9%
423
0.9%
431
 
0.3%
445
1.4%
455
1.4%
464
1.1%
ValueCountFrequency (%)
1171
 
0.3%
921
 
0.3%
901
 
0.3%
891
 
0.3%
881
 
0.3%
872
0.6%
861
 
0.3%
854
1.1%
841
 
0.3%
832
0.6%

temp_1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.70114943
Minimum35
Maximum117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-27T22:33:00.195199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile43.35
Q154
median62.5
Q371
95-th percentile81.65
Maximum117
Range82
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.12054244
Coefficient of variation (CV)0.1933065431
Kurtosis0.3390438597
Mean62.70114943
Median Absolute Deviation (MAD)8.5
Skewness0.2551268251
Sum21820
Variance146.9075491
MonotonicityNot monotonic
2022-09-27T22:33:00.373836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5716
 
4.6%
6016
 
4.6%
6815
 
4.3%
6513
 
3.7%
7112
 
3.4%
5512
 
3.4%
6711
 
3.2%
5211
 
3.2%
6411
 
3.2%
5911
 
3.2%
Other values (46)220
63.2%
ValueCountFrequency (%)
352
 
0.6%
361
 
0.3%
393
0.9%
405
1.4%
413
0.9%
423
0.9%
431
 
0.3%
444
1.1%
455
1.4%
464
1.1%
ValueCountFrequency (%)
1171
 
0.3%
921
 
0.3%
901
 
0.3%
891
 
0.3%
881
 
0.3%
872
0.6%
861
 
0.3%
854
1.1%
841
 
0.3%
832
0.6%

average
Real number (ℝ≥0)

HIGH CORRELATION

Distinct243
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.76063218
Minimum45.1
Maximum77.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-27T22:33:00.555883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum45.1
5-th percentile45.5
Q149.975
median58.2
Q369.025
95-th percentile77
Maximum77.4
Range32.3
Interquartile range (IQR)19.05

Descriptive statistics

Standard deviation10.52730643
Coefficient of variation (CV)0.1761578826
Kurtosis-1.315523428
Mean59.76063218
Median Absolute Deviation (MAD)9.25
Skewness0.2320463885
Sum20796.7
Variance110.8241806
MonotonicityNot monotonic
2022-09-27T22:33:00.738898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.17
 
2.0%
77.37
 
2.0%
45.24
 
1.1%
45.34
 
1.1%
77.14
 
1.1%
48.43
 
0.9%
77.43
 
0.9%
77.23
 
0.9%
49.13
 
0.9%
76.93
 
0.9%
Other values (233)307
88.2%
ValueCountFrequency (%)
45.17
2.0%
45.24
1.1%
45.34
1.1%
45.42
 
0.6%
45.52
 
0.6%
45.62
 
0.6%
45.72
 
0.6%
45.81
 
0.3%
45.92
 
0.6%
462
 
0.6%
ValueCountFrequency (%)
77.43
0.9%
77.37
2.0%
77.23
0.9%
77.14
1.1%
772
 
0.6%
76.93
0.9%
76.82
 
0.6%
76.72
 
0.6%
76.62
 
0.6%
76.51
 
0.3%

actual
Real number (ℝ≥0)

HIGH CORRELATION

Distinct55
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.54310345
Minimum35
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-27T22:33:00.937788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile42.35
Q154
median62.5
Q371
95-th percentile81
Maximum92
Range57
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.79414614
Coefficient of variation (CV)0.1885762856
Kurtosis-0.5638304613
Mean62.54310345
Median Absolute Deviation (MAD)8.5
Skewness0.02343610814
Sum21765
Variance139.1018831
MonotonicityNot monotonic
2022-09-27T22:33:01.123467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5716
 
4.6%
6816
 
4.6%
6016
 
4.6%
6513
 
3.7%
7112
 
3.4%
5512
 
3.4%
6711
 
3.2%
6411
 
3.2%
5911
 
3.2%
5211
 
3.2%
Other values (45)219
62.9%
ValueCountFrequency (%)
352
 
0.6%
361
 
0.3%
393
0.9%
406
1.7%
413
0.9%
423
0.9%
431
 
0.3%
444
1.1%
454
1.1%
464
1.1%
ValueCountFrequency (%)
921
 
0.3%
901
 
0.3%
891
 
0.3%
881
 
0.3%
872
0.6%
861
 
0.3%
854
1.1%
841
 
0.3%
832
0.6%
823
0.9%

forecast_noaa
Real number (ℝ≥0)

HIGH CORRELATION

Distinct37
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.23850575
Minimum41
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-27T22:33:01.309326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile43
Q148
median56
Q366
95-th percentile75
Maximum77
Range36
Interquartile range (IQR)18

Descriptive statistics

Standard deviation10.60574637
Coefficient of variation (CV)0.1852904129
Kurtosis-1.235549844
Mean57.23850575
Median Absolute Deviation (MAD)9
Skewness0.2506190341
Sum19919
Variance112.481856
MonotonicityNot monotonic
2022-09-27T22:33:01.480022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
4519
 
5.5%
4416
 
4.6%
4916
 
4.6%
4815
 
4.3%
4614
 
4.0%
4714
 
4.0%
4312
 
3.4%
6212
 
3.4%
6412
 
3.4%
6611
 
3.2%
Other values (27)207
59.5%
ValueCountFrequency (%)
415
 
1.4%
426
 
1.7%
4312
3.4%
4416
4.6%
4519
5.5%
4614
4.0%
4714
4.0%
4815
4.3%
4916
4.6%
508
2.3%
ValueCountFrequency (%)
774
 
1.1%
7610
2.9%
758
2.3%
748
2.3%
738
2.3%
729
2.6%
718
2.3%
705
1.4%
695
1.4%
686
1.7%

forecast_acc
Real number (ℝ≥0)

HIGH CORRELATION

Distinct37
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.37356322
Minimum46
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-27T22:33:01.666220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile48
Q153
median61
Q372
95-th percentile79
Maximum82
Range36
Interquartile range (IQR)19

Descriptive statistics

Standard deviation10.54938117
Coefficient of variation (CV)0.1691322514
Kurtosis-1.30411627
Mean62.37356322
Median Absolute Deviation (MAD)9
Skewness0.1973335282
Sum21706
Variance111.2894432
MonotonicityNot monotonic
2022-09-27T22:33:01.845988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5118
 
5.2%
7816
 
4.6%
5015
 
4.3%
5215
 
4.3%
4913
 
3.7%
5313
 
3.7%
5613
 
3.7%
5913
 
3.7%
4812
 
3.4%
7712
 
3.4%
Other values (27)208
59.8%
ValueCountFrequency (%)
466
 
1.7%
477
 
2.0%
4812
3.4%
4913
3.7%
5015
4.3%
5118
5.2%
5215
4.3%
5313
3.7%
5412
3.4%
556
 
1.7%
ValueCountFrequency (%)
821
 
0.3%
816
 
1.7%
805
 
1.4%
799
2.6%
7816
4.6%
7712
3.4%
7610
2.9%
758
2.3%
745
 
1.4%
7310
2.9%

forecast_under
Real number (ℝ≥0)

HIGH CORRELATION

Distinct36
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.77298851
Minimum44
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-27T22:33:02.031465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile45.35
Q150
median58
Q369
95-th percentile77
Maximum79
Range35
Interquartile range (IQR)19

Descriptive statistics

Standard deviation10.70525553
Coefficient of variation (CV)0.1790985493
Kurtosis-1.261266441
Mean59.77298851
Median Absolute Deviation (MAD)9
Skewness0.2534595539
Sum20801
Variance114.6024959
MonotonicityNot monotonic
2022-09-27T22:33:02.197809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
4924
 
6.9%
4618
 
5.2%
5517
 
4.9%
5013
 
3.7%
4812
 
3.4%
5112
 
3.4%
6512
 
3.4%
6612
 
3.4%
7712
 
3.4%
4712
 
3.4%
Other values (26)204
58.6%
ValueCountFrequency (%)
449
 
2.6%
459
 
2.6%
4618
5.2%
4712
3.4%
4812
3.4%
4924
6.9%
5013
3.7%
5112
3.4%
529
 
2.6%
539
 
2.6%
ValueCountFrequency (%)
797
2.0%
789
2.6%
7712
3.4%
768
2.3%
7511
3.2%
745
1.4%
739
2.6%
725
1.4%
7111
3.2%
707
2.0%

friend
Real number (ℝ≥0)

HIGH CORRELATION

Distinct66
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.03448276
Minimum28
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2022-09-27T22:33:02.392574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile35
Q147.75
median60
Q371
95-th percentile86.65
Maximum95
Range67
Interquartile range (IQR)23.25

Descriptive statistics

Standard deviation15.62617938
Coefficient of variation (CV)0.2602867328
Kurtosis-0.7053289416
Mean60.03448276
Median Absolute Deviation (MAD)11
Skewness0.120703434
Sum20892
Variance244.1774819
MonotonicityNot monotonic
2022-09-27T22:33:02.575284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5614
 
4.0%
6513
 
3.7%
7012
 
3.4%
5812
 
3.4%
6112
 
3.4%
6411
 
3.2%
6211
 
3.2%
5710
 
2.9%
5410
 
2.9%
419
 
2.6%
Other values (56)234
67.2%
ValueCountFrequency (%)
281
 
0.3%
292
 
0.6%
302
 
0.6%
311
 
0.3%
332
 
0.6%
345
1.4%
356
1.7%
363
0.9%
373
0.9%
387
2.0%
ValueCountFrequency (%)
953
0.9%
941
 
0.3%
932
 
0.6%
911
 
0.3%
904
1.1%
892
 
0.6%
882
 
0.6%
873
0.9%
863
0.9%
855
1.4%

Interactions

2022-09-27T22:32:56.099101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:39.964240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:41.731932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:43.925242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:45.499282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:47.096117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:48.752872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:50.586582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:52.399794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:54.179987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:56.264025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:40.128828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:41.895553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:44.076497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:45.655212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:47.249036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:48.923313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:50.754631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:52.594454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:54.346802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:56.424834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:40.294434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:42.127137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:44.230906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:45.816372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:47.429117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:49.314292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:50.922701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:52.773640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:54.532358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:56.581380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:40.445983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:42.320032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:44.381759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:45.976239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:47.584457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:49.466368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:51.089636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:52.938659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:54.709507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:56.737957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:40.597667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:42.484416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:44.528993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:46.122928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:47.747198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:49.612983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:51.247544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:53.099537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:55.097185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:56.896564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:40.774385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:42.653805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:44.688795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:46.280371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:47.925899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:49.765284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:51.412650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:53.270856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:55.263917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:57.042335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:40.930812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:42.815420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:44.842053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:46.439631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:48.080992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:49.928658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:51.625797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:53.476538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:55.428361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:57.204345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:41.108327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:43.110286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:45.007962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:46.600363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:48.251990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:50.092566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:51.847720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:53.658762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:55.604663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:57.363047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:41.391245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:43.315295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:45.165824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:46.770861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:48.425324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:50.259549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:52.024871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:53.831986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:55.779310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:57.533321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:41.564425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:43.746464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:45.336736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:46.943196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:48.588522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:50.428051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:52.209398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:54.004732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T22:32:55.941968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-09-27T22:33:02.756451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-27T22:33:03.002942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-27T22:33:03.240924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-27T22:33:03.459651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-27T22:33:03.612364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-27T22:32:57.789754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-27T22:32:58.104184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

yearmonthdayweektemp_2temp_1averageactualforecast_noaaforecast_accforecast_underfriend
0201611Fri454545.64543504429
1201612Sat444545.74441504461
2201613Sun454445.84143464756
3201614Mon444145.94044484653
4201615Tues414046.04446464641
5201616Wed404446.15143494840
6201617Thurs445146.24545494638
7201618Fri514546.34843474634
8201619Sat454846.45046504547
92016110Sun485046.55245484849

Last rows

yearmonthdayweektemp_2temp_1averageactualforecast_noaaforecast_accforecast_underfriend
33820161222Thurs514945.14542474638
33920161223Fri494545.14045494435
34020161224Sat454045.14144474639
34120161225Sun404145.14242494431
34220161226Mon414245.24245484658
34320161227Tues424245.24741504747
34420161228Wed424745.34841494458
34520161229Thurs474845.34843504565
34620161230Fri484845.45744464442
34720161231Sat485745.54042484757